EMBO Journal 2011 Cover Contest

scientific image entry - a hive panel

▲ The non-scientific entry is abstract photo of fiber optics. The scientific entry was an information graphic showing a hive panel of genomic annotations in human, mouse and dog genomes. The hive panel is based on the use of the newly introduced hive plot.

About the EMBO Journal Cover Contest

The EMBO Journal non-scientific cover prize is awarded for the most interesting and beautiful image made outside the lab. Contestants may submit, for example, photos or artistic impressions of wildlife animals, plants or landscapes. Particularly welcome will also be hand or computer-generated paintings or drawings (or photographs of other works of art) related to a biological or molecular biological topic.

The EMBO Journal scientific cover prize is awarded for the most captivating and thought-provoking contribution depicting a piece of molecular biology research. Entries can include light or electron micrographs, 3D reconstructions or models of biological specimen or molecules, spectacular artefacts collected in the lab, original new views of lab equipment (but not of colleagues!), or other research-based images to be of interest to molecular biologists.

In 2010 EMBO selected my submission of a large Circos figure for its cover (see right). Front page exposure of this sort has made Circos a very popular tool for visualization in genomics, and in particular, in cancer research where there is a need to illustrate differences between genomes.

My other entry for the 2011 cover contest was a non-scientific abstract image photo of fiber optics.

Current State of Network Visualization

A large number of layout algorithms already exist to attempt to visualize networks. In an attempt to create attractive layouts, node and edge positions are optimized to minimize some fitness function, such as overlap or force (if edges are treated as springs). Unfortunately, as a result it is impossible to relate the position of a node (or the distance between any two nodes in the layout) to their connected neighbourhood in the network. This particularly holds for large networks, where nodes and edge overlap in the layout is unavoidable.

▲ Hairballs are irrational network visualizations. Shown here are 8 different layouts of the same network — it is impossible to identify that these images correspond to the same network. More importantly, it is very difficult to extract meaningful and quantitative information from these layouts.
(Hive plots solve this problem.)

The Hive Plot

Hive Plots for Networks

The hive plot is a rational approach to visualizing networks. It is designed to complement (at times, replace) the network hairball.

In a hive plot, network nodes are assigned to and placed on axes using rational rules. These rules typically are a function of local network structure around the node (connectivity, density, centrality, etc). The resulting plot is interpretable.

▲ In a hive plot, nodes in a network are assigned and placed on axes using properties of the node and its relationship to its neighbours. The resulting layout is rational and easily interpreted, because the rules are based on meaningful quantities.
(Hive plots rationalize network visualization.)

Hive Plots for Ratios

The hive plot can be applied to visualize a large number of ratios between three or more scales.

Instead of network edges, the lines in a hive plot now correspond to an (x,y) data pair, which can be interpreted as a ratio (x/y). This approach is particularly effective when lines are drawn as ribbons, which are then stacked. This is shown in the figure below.

The resulting visualization bears resemblance to a stacked bar plot. The circular layout grants the advantage of being able to instantly compare all pair-wise comparisons between the axes (when three axes are used). This layout also gives the image a compare compact feel and is particularly suitable for tiling.

In the examples below, a 3-axis hive plot is shown with 8 ratios between each axis. The ratios are independent, in the sense that corresponding ribbons (e.g. blue) may have different thickness on either side of an axis. For example, if x:z = 2:3 and x:y = 1:3 then the ribbon on the left of the x axis will be twice as thick as on the right (see black arrow in figure below).

▲ In a dual scale hive plot, each axis supports two groups of independent ribbons. Axes can be hidden (A), shown (B), or split by various amounts (C 20deg, D 30deg, E 40deg, F 60deg) to explicitly show the transition between ribbons on either side of the axis. Download high-resolution panels
ABCDEF
(Hive plots are useful for visualizing ratios.)

The axes in a hive plot can be arranged arbitrarily. In the figure above panels A and B show 24 ratios — 8 each between x/y, x/z, and y/x axes. In panels C-F each axis is split to create a single 6-axis plot from a dual 3-axis plot. The split axes reveal the transition between ribbons from the left and right sides.

The dual 3-axis plot appears more stylized and mathematical, whereas the single 6-axis plot is softer and organic. As the axis split distance is increased, the plots begin to look like surface density maps, which to some degree occludes the relationships between the ratio ribbons.

Comparing Genome Annotation

For each of human (hg18), mouse (mm8) and dog (canfam2) genome assemblies, UCSC annotations, available for each genome from the table browser, were used to hierarchically organize each base in the assembly using the following criteria: gene, repeat and gene+repeat. For each of these, bases were further categorized as conserved or not.

By exhaustively intersecting each of the annotation regions, the assembly was divided into disjoint segments, each with its annotation states. For example, below are a few adjacent regions from hg18 chr1 (a assembly, r repeat, c-cf conserved with dog, c-mm conserved with mouse).

Next, the total size of regions for each combination of annotation was calculated for each pairwise combination of genomes. The second genome in the pair dictates which conservation is used. For example, for the human-mouse pair, the relative fractions of the human genome that fall into each of the categories are

Using these two lists, all the ratios between the human and mouse axes can be determined. For example, for the conserved/gene/non-repeat regions the ratio of human:mouse is 0.00155:0.00160 (lines are bolded above). The corresponding ribbon for this ratio is shown below.

▲ The ratio of conserved gene regions not in repeats between human and mouse genomes.
(More about hive plots.)

Category assignment into repeat, gene and conserved region was parametrized into three ranges for each criteria. These values were selected heuristically, to obtain a reasonable sample for each combination.

geneg1 <4kb, g2 4kb-22kb, g3 >22kb

repeatr1 simple, r2 LTR, r3 LINE/SINE

conservationc1 <45%, c2 45%-58%, c3 >58%

Given 3 parameters for each of the categories, the full comparison is represented by 27 hive plots. These plots are arranged on the cover as follows

▲ The ratio of conserved gene regions not in repeats between human and mouse genomes.
(More about hive plots.)

The scale of the axes was logarithmic to maintain visibility of all categories.

The final cover designs for the cluster of 27 hive plots are shown below.

The split plot design originated in agriculture, where applying some factors on a small scale is more difficult than others. For example, it's harder to cost-effectively irrigate a small piece of land than a large one. These differences are also present in biological experiments. For example, temperature and housing conditions are easier to vary for groups of animals than for individuals.

The split plot design is an expansion on the concept of blocking—all split plot designs include at least one randomized complete block design. The split plot design is also useful for cases where one wants to increase the sensitivity in one factor (sub-plot) more than another (whole plot).